All publications herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Over the years, many different techniques have been developed and applied for imaging kidneys. Early techniques involved radiation-opaque dyes injected into a patient followed by a time series of X-rays that allowed a diagnostician to observe the passage of the radiation-opaque dyes through the kidney. These techniques were entirely manual, requiring physical display of the images and laborious interpretation by the diagnostician. More recently, automated imaging techniques have been developed to provide high-resolution, three-dimensional images of various organs.
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive way to gain insight into both the anatomic and functional conditions of the kidney. Because previous studies have revealed a good correlation between MRI and radionuclear scintigraphy results, DCE-MRI has the potential to be the predominant method in kidney diagnosis. Moreover, this technique is especially beneficial for children because it does not use radioactivity and provides higher resolutions than nuclear scans. The quantitative assessment based on the renal DCE-MRI is foreshadowed in pediatric diagnosis.
Both morphological and functional parameters are important for clinical decisions in pediatric kidney diseases. For morphological features, kidney volume is important and computationally accessible, which is effective in disease decision trees. Nearly all patients diagnosed will undergo a renal size measurement (3). As well, the shape and position of the cortex, medulla and other tissues provide useful information for diagnosis.
To acquire functional parameters, kinetic models are used and studied. Based on first order kinetics, two compartment models, which include a vascular and a tubular compartment, are usually used to simulate the renal physical mechanisms of the kidney. The flux is considered to be unidirectional in the two compartment model through glomerular filtration, which can be measured by the Patlak-Ruland plot technique. In later research, the outflow from the tubules is taken into account, which has an improved correlation with clearance results. Recently, the multiple compartment model is also proposed and the preliminary results are reported, which concern a more detailed description of the nephron system.
However, the complexity of renal physiology and variance in imaging techniques sometimes complicates the implementation of a sophisticated model approaching a realistic kidney, which limits the application of those models. The addition of compartments does not necessarily result in an increase of performance. Therefore, model free methods are adopted as well.
The model free methods mainly focus on the individual time intensity curves (TICs). Many parameters such as mean transit time and maximum upslope can be directly calculated from TICs. According to the curve shape, model free methods try to distinguish normal tissue from abnormal tissue, which in turn is related to pathological findings. Compared to physiological models, model free methods are less sensitive to the protocol and supply a more detailed pixel by pixel (or voxel) analysis.
To create a flexible and comprehensive assessment tool for pediatric renal disease studies, the inventors employed a cluster based method and presented the post processing procedures. The major 4 stages of the post processing are displayed in FIG. 1. After preprocessing, the dynamical data are grouped into three dimensional (3D) clusters according to their activity similarities as judged by time intensity curves. When applying the anatomical and functional knowledge, the clusters are automatically recognized to be either part of the kidney or not, and then the kidney is segmented into different inner compartments. In the third stage, both global and local parameters are calculated with optional definitions. Finally, both anatomical and functional features are displayed in an intuitive form for visualization by clinicians.
Compared to the traditional two dimensional (2D) regions of interest (ROI), the 3D clusters have a more precise description of similar tissues while reducing the number of areas of interests. The automatic segmentation based on clusters has a greater capacity to deal with misshapen tissues due to the disorders, which alleviates clinicians from tedious manual delineations. The cluster number can be adjusted and parameters can be selected for different research purposes and clinical protocols.